A multiple criteria approach for the evaluation of the rail transit networks in Istanbul

The deficiencies in the Istanbul transportation system have led the local authorities to plan several alternative transportation projects. In this paper three alternative rail transit network proposals are evaluated by using Analytic Hierarchy Process (AHP), a multiple criteria decision support system. The AHP facilitates decision-making by organizing perceptions, experiences, knowledge and judgments, the forces that influence the decision, into a hierarchical framework with a goal, scenarios, criteria and alternatives of choice. Based on this analysis, the decision makers have developed a new alternative as a combination of the most closely competing two alternative rail transit networks. This combination rail transit network is currently under construction.     

A hierarchical line planning approach for a large-scale high speed rail network: The China case

Planning a set of train lines in a large-scale high speed rail (HSR) network is typically influenced by issues of longer travel distance, high transport demand, track capacity constraints, and a non-periodic timetable. In this paper, we describe an integrated hierarchical approach to determine line plans by defining the stations and trains according to two classes. Based on a bi-level programming model, heuristics are developed for two consecutive stages corresponding to each classification. The approach determines day-period based train line frequencies as well as a combination of various stopping patterns for a mix of fast trunk line services between major stations and a variety of slower body lines that offer service to intermediate stations, so as to satisfy the predicted passenger transport demand. Efficiencies of the line plans described herein concern passenger travel times, train capacity occupancy, and the number of transfers. Moreover, our heuristics allow for combining many additional conflicting demand–supply factors to design a line plan with predominantly cost-oriented and/or customer-oriented objectives. A range of scenarios are developed to generate three line plans for a real-world example of the HSR network in China using a decision support system. The performance of potential train schedules is evaluated to further examine the feasibility of the obtained line plans through graphical timetables.     

A review and analysis of the investment in,and cost structure of,intermodal rail terminals

The results presented in this article identify the role of costs in the scientific and grey freight terminal handling literature and analyses the handling costs of different terminal sizes. The literature review shows that handling costs only play a marginal role in the scientific research in intermodal rail freight terminals (IRT). This is remarkable given the large role costs occupy in decision-making in freight transport. Furthermore, the used cost levels show a wide range of proposed amounts and terminal sizes or handling technologies are seldom addressed. Finally, many of the scientific papers do not make it clear whether the average transhipment cost or market price is referred to. Next, the analysis of the investment in, and cost structure of, IRTs shows that IRT investments are very capital-intensive leading to relatively high average costs per handling. However, given the cost characteristics of IRTs, the average cost per handling represents the underlying cost structure and are – in this sense – representative. The cost analysis demonstrates that extra-large IRTs actually have the lowest average handling costs, followed by small IRTs.     

Fuzzy modeling approach to the rail freight car inventory problem

Techniques to improve freight car fleet use are of considerable interest to the railroad industry. In this paper, we present a fuzzy inventory control approach applied to the sizing of empty cars on a rail network. We address the problem of deciding the optimal inventory level and the optimal ordering quantity for a rail freight car fleet system in which demand and travel time are uncertain variables represented as triangular fuzzy numbers. Based on the fuzzy economic order quantity (EOQ) formula, a modified fuzzy EOQ model is set up and the optimal policy is developed using the signed distance method to defuzzify the fuzzy total cost. Computational results made for the Serbian rail network case verify the proposed model as well as the efficiency of the approach.     

Incorporating sustainability assessment in transportation planning: an urban transportation vehicle-based approach

Environmental assessments are on the critical path for the development of land, infrastructure and transportation systems. These assessments are based on planning methods which, in turn, are subject to continuous enhancement. The substantial impacts of transportation on environment, society and economy strongly urge the incorporation of sustainability into transportation planning. Two major developments that enhance transportation sustainability are new fuels and vehicle power systems. Traditional planning ignores technology including the large differences among conventional, hybrid and alternative fuel vehicles and buses. The introduction of alternative fuel vehicles is likely to change the traditional transportation planning process because different characteristics need to be taken into account. In this study a sustainability framework is developed that enables assessment of transportation vehicle characteristics. Identified indicators are grouped in five sustainability dimensions (Environment, Technology, Energy, Economy and Users). Our methodology joins life cycle impacts and a set of quantified indicators to assess the sustainability performance of seven popular light-duty vehicles and two types of transit buses. Bus Rapid Transit receives the highest sustainability index and the pickup truck the lowest. Hybrid electric vehicles are found to have the highest sustainability index among all other passenger vehicles. A sensitivity analysis shows the proposed sustainability dimensions produce robust sustainability assessment for several weighting scenarios. The results are both technology and policy sensitive, thus useful for both short- and long-term planning.     

Passenger-demands-oriented train scheduling for an urban rail transit network

This paper considers the train scheduling problem for an urban rail transit network. We propose an event-driven model that involves three types of events, i.e., departure events, arrival events, and passenger arrival rates change events. The routing of the arriving passengers at transfer stations is also included in the train scheduling model. Moreover, the passenger transfer behavior (i.e., walking times and transfer times of passengers) is also taken into account in the model formulation. The resulting optimization problem is a real-valued nonlinear nonconvex problem. Nonlinear programming approaches (e.g., sequential quadratic programming) and evolutionary algorithms (e.g., genetic algorithms) can be used to solve this train scheduling problem. The effectiveness of the event-driven model is evaluated through a case study.     

Estimating the influence of common disruptions on urban rail transit networks

With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented.     

Development of efficient stop planning optimization process for high‐speed rail systems

The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta‐heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high‐speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real‐world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high‐speed rail system planners in designing optimal stopping patterns that correspond to passenger demand. Copyright © 2017 John Wiley & Sons, Ltd.     

Trucking industry demand for urban shared use freight terminals

The issue of shared use urban freight facilities first received attention during the 1970s when it was observed that, while inter-urban freight movements were becoming increasingly efficient, there were significant diseconomies in the movement of freight via truck within urban areas. Early research suggested that shared urban freight facilities should be constructed so that trucking companies could consolidate smaller shipments into larger ones. In the past few years, the concept of Urban Ports has gained increasing attention, not just for carriers who need to load and unload freight, but to provide a place near the urban center for truckers to wait out peak traffic periods. In this paper, using recently developed survey data, we examine trucking company interest in such facilities by examining the results of an ordered probit demand model.     

Equity-oriented skip-stopping schedule optimization in an oversaturated urban rail transit network

In this study, we focus on improving system-wide equity performance in an oversaturated urban rail transit network based on multi-commodity flow formulation. From the system perspective, an urban rail transit network is a distributed system, where a set of resources (i.e., train capacity) is shared by a number of users (i.e., passengers), and equitable individuals and groups should receive equal shares of resources. However, when oversaturation occurs in an urban rail transit network during peak hours, passengers waiting at different stations may receive varying shares of train capacity leading to the inequity problem under train all-stopping pattern. Train skip-stopping pattern is an effective operational approach, which holds back some passengers at stations and re-routes their journeys in the time dimension based on the available capacity of each train. In this study, the inequity problem in an oversaturated urban rail transit network is analyzed using a multi-commodity flow modeling framework. In detail, first, discretized states, corresponding to the number of missed trains for passengers, are constructed in a space-time-state three-dimensional network, so that the system-wide equity performance can be viewed as a distribution of all passengers in different states. Different from existing flow-based optimization models, we formulate individual passenger and train stopping pattern as commodity and network structure in the multi-commodity flow-modeling framework, respectively. Then, we aim to find an optimal commodity flow and well-designed network structure through the proposed multi-commodity flow model and simultaneously achieve the equitable distribution of all passengers and the optimal train skip-stopping pattern. To quickly solve the proposed model and find an optimal train skip-stopping pattern with preferable system-wide equity performance, the proposed linear programming model can be effectively decomposed to a least-cost sub-problem with positive arc costs for each individual passenger and a least-cost sub-problem with negative arc costs for each individual train under a Lagrangian relaxation framework. For application and implementation, the proposed train skip-stopping optimization model is applied to a simple case and a real-world case based on Batong Line in the Beijing Subway Network. The simple case demonstrates that our proposed Lagrangian relaxation framework can obtain the approximate optimal solution with a small-gap lower bound and a lot of computing time saved compared with CPLEX solver. The real-world case based on Batong Line in the Beijing Subway Network compares the equity and efficiency indices under the operational approach of train skip-stopping pattern with those under the train all-stopping pattern to state the advantage of the train skip-stopping operational approach.     

Forecasting high-speed rail ridership using a simultaneous modeling approach

Abstract

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.     

Real-time automatic rescheduling strategy for an urban rail line by integrating the information of fault handling

In the operation of urban rails, faults are inevitable, which leads to deviation between the actual timetable and the planned timetable. In nowadays, timetable rescheduling strategies rarely integrate the information of fault handling. In this paper, we develop a real-time automatic rescheduling strategy, which integrates the dynamic information of fault handling. The rescheduled timetable is obtained by a mathematical optimization model, the constraints set of which is automatically generated and adjusted as more information of fault handling is feedback. Compared with the experience-based rescheduling methods, the automatic rescheduling strategy reacts more quickly, and uses the information of fault handling more efficiently. A simulation system for testing the automatic rescheduling strategy is built, which uses the data of the Beijing Yizhuang metro line. Via testing on the simulation system, the effectiveness and efficiency of the automatic rescheduling strategy are validated.     

Area-wide urban traffic control: A Bee Colony Optimization approach

The paper describes a new method of optimizing traffic signal settings. The area-wide urban traffic control system developed in the paper is based on the Bee Colony Optimization (BCO) technique. The BCO method is based on the principles of the collective intelligence applied by the honeybees during the nectar collecting process. The optimal (or near-optimal) values of cycle length, offsets, and splits are discovered by minimizing the total travel time of all network users travelling through signalized intersections. The set of numerical experiments is performed on well-known traffic benchmark network. The results obtained by the BCO approach are compared with the results found by Simulated Annealing (SA). It has been shown that the suggested BCO approach outperformed the SA.     

Optimizing train operational plan in an urban rail corridor based on the maximum headway function

The train operational plan (TOP) plays a crucial role in the efficient and effective operation of an urban rail system. We optimize the train operational plan in a special network layout, an urban rail corridor with one terminal yard, by decomposing it into two sub-problems, i.e., the train departure profile optimization and the rolling stock circulation optimization. The first sub-problem synthetically optimizes frequency setting, timetabling and the rolling stock circulation at the terminal without a yard. The maximum headway function is generated to ensure the service of the train operational plan without considering travel demand, then we present a model to minimize the number of train trips, and design a heuristic algorithm to maximize the train headway. On the basis of a given timetable, the rolling stock circulation optimization only involves the terminal with a yard. We propose a model to minimize the number of trains and yard–station runs, and an algorithm to find the optimal assignment of train-trip pair connections is designed. The computational complexities of the two algorithms are both linear. Finally, a real case study shows that the train operational plan developed by our approach enables a better match of train headway and travel demand, and reduces the operational cost while satisfying the requirement of the level of service.     

基于乘客寻路行为的城市轨道交通车站导向标识序化设置研究

为实现轨道交通车站内客流快速疏散,避免因乘客滞留造成站内乘客出行效率低以及大客流压力导致的安全隐患等问题,本文对目前国内导向标识的设置原则及功能进行描述,依托大数据等信息化技术分析行人寻路行为机理及出行特征与导向标识序化设置间的关系,研究导向标识的序化设置,依据行人在不同交通设施的步行速度及信息处理时间,并提出在站内停顿点数量较多的通道、楼梯口及闸机处设置导向标识的位置,进而对导向标识的设置进行人性化和合理化的优化设置,对轨道车站内停顿点位置进行导向标识的合理布设,以快速引导行人进行出行决策,减少停顿点数量。     

A Value-at-Risk (VAR) approach to routing rail hazmat shipments

Hazardous materials (hazmat) accidents are rare though the consequences could be disastrous. Given the possibility of low probability – high consequence event, a risk-averse routing hazmat shipment is necessary. We propose a value-at-risk (VaR) approach to route rail hazmat shipments, using the best train configuration, over a given railroad network with limited number of train services such that the transport risk as measured by VaR is minimized. Freight train derailment reports of the Federal Railroad Administration were analyzed to develop expressions that would incorporate characteristics of railroad accidents, and then to estimate the different inputs. The proposed methodology was used to study several problem instances generated using the realistic network of a railroad operator, and to demonstrate that it is possible to develop different routes for shipments depending on the risk preference of the decision maker.     

Modeling access to public transport in urban areas

It is important to measure public transport accessibility to help improve the sustainability of transport systems in metropolitan areas. Although many studies have defined different approaches for measuring public transport accessibility, there have been limited methods developed for measuring accessibility levels that incorporate spatial aspects. Population density is an important distributional indicator that has also been ignored in previous methods developed for quantifying accessibility. This paper outlines the research context for measurement of public transport accessibility and then describes a methodology developed as well as an application the Public Transport Accessibility Index in Melbourne area, Australia. Using the Victorian Integrated Survey of Travel and Activity dataset, we applied separate‐ordered logit regression models to examine how the new index performs with a series of predictor variables compared with two existing approaches. Key findings indicate that there is a higher probability of public transport patronage in areas with higher levels of accessibility. Furthermore, it was found using statistical modelling that the new index produces better results compared with previous approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

The impact of road traffic noise on urban protected areas: A landscape modeling approach

We modeled the propagation of traffic noise over the landscape and analyzed its impact on the structure and configuration of protected areas of the Twin Cities Metro Region, Minnesota. Using four noise thresholds, we found that at low and medium noise levels, 19% and 11% of the protected areas are within the road-effect zone. Using mean patch area and patch shape index, we measured the acoustic fragmentation of habitats. We found that at higher levels of noise patch shape index increased, while mean patch size decreased. The acoustic diversity of a patch is also found to be correlated with land cover type, patch area, and patch shape.     

A whole-system approach to evaluating urban transit investments

New transit capital expenditures are typically evaluated in isolation from the transit/transport systems to which they belong. Problems with reporting performance elements such as ridership and costs are discussed. A focus on evaluating the total transport systems impact of new transit project implementation is called for. On this basis, new US rail transit systems have generally performed poorly. Total transit ridership has generally shown only minimal improvements and, at times, has declined. Financial performance has been disappointing in most cases, particularly when understood in the context of the additional system costs imposed through the reconfiguration of bus networks to serve the new rail systems. Low-cost approaches to improving basic transit services can often be more effective than either rail or bus capital-based projects. An obsession with technology leads to the wrong questions being asked. We should instead start inquiry with the study of needs.